RSFLoc: Robust Smart Fingerprint Localization-Based Hybrid PSO– ANN Approach in Complex Environment
Keywords:
PL, RSSI, ANN, PSO, fingerprint localization, MSEAbstract
The rapid proliferation of Internet of Things (IoT)-enabled wireless localization systems has gained prominence in electrical engineering applications, particularly in domains such as healthcare, where precise indoor positioning is essential for tracking personnel and assets. This paper introduces the Robust Smart Fingerprint Localization (RSFLoc) model, leveraging Received Signal Strength Indicator (RSSI) fingerprinting in wireless communication networks. The model enhances localization accuracy through the optimization of an Artificial Neural Network (ANN) using Particle Swarm Optimization (PSO), addressing key signal processing challenges like multipath propagation, shadowing, and environmental dynamics in indoor environments. In the offline phase, RSSI data from multiple Access Points (APs) across diverse zones are rigorously analyzed to evaluate signal robustness and model path loss (PL) characteristics. The online phase deploys the PSO-optimized ANN on a simulated ESP32 microcontroller interfaced with MATLAB/Simulink for real-time x-y coordinate estimation. Localization results are transmitted via IoT protocols to the ThingSpeak cloud platform, enabling visualization and remote monitoring through a mobile application. Experimental results demonstrate high fidelity between measured and estimated PL curves, with a low Mean Square Error (MSE) of 1.001 dB for RSSI-driven PL modeling (ranging from 3.2723 × 10^{-4} to 1.001 dB). The ANN model's validation MSE achieves 2.0956 m, outperforming training (MSE: 0.0557 m post-optimization) and testing phases due to improved hyperparameter tuning via PSO, which enhances convergence stability. Regression analysis reveals strong linearity (R² > 0.95) between predicted and actual locations, while error histograms indicate an 85% reduction in localization error compared to baseline methods, underscoring the model's efficacy in practical wireless communication systems.
References
V. Choudhary, P. Guha, G. Pau, S. J. E. Mishra, and S. Indicators, "An overview of smart agriculture using internet of things (IoT) and web services," p. 100607, 2025.
S. J. I. i. I. P. S. Pešić, "Modern Challenges in Indoor Positioning Systems: AI to the Rescue," p. 65, 2024.
S. C. Narasimman and A. J. I. S. J. Alphones, "DumbLoc: Dumb indoor localization framework using Wi-Fi fingerprinting," vol. 24, no. 9, pp. 14623-14630, 2024.
Z. Luo, W. Li, Y. Wu, H. Dong, L. Bian, and W. J. I. I. o. T. J. Wang, "Accurate indoor localization for bluetooth low energy backscatter," 2024.
W. Zhao, A. Goudar, M. Tang, and A. P. J. a. p. a. Schoellig, "Ultra-wideband time difference of arrival indoor localization: from sensor placement to system evaluation," 2024.
J. Li, S. Yu, Z. Wei, and Z. J. S. Zhou, "An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE," vol. 25, no. 9, p. 2947, 2025.
K. Choutri, M. Lagha, S. Meshoul, H. Shaiba, A. Chegrani, and M. J. S. Yahiaoui, "Vision-based UAV detection and localization to indoor positioning system," vol. 24, no. 13, p. 4121, 2024.
A. Girgensohn, M. Patel, J. T. J. P. Biehl, and U. Computing, "Radio-frequency-based indoor-localization techniques for enhancing Internet-of-Things applications," vol. 28, no. 1, pp. 385-401, 2024.
Y. Bai, "Development of a WiFi and RFID based indoor location and mobility tracking system," RMIT University, 2024.
S. Huang, K. Zhao, Z. Zheng, W. Ji, T. Li, and X. Liao, "An optimized fingerprinting-based indoor positioning with Kalman filter and universal kriging for 5G internet of things," Wireless Communications and Mobile Computing, vol. 2021, pp. 1-10, 2021.
S. L. Ayinla, A. Abd Aziz, M. Drieberg, M. Susanto, and M. J. I. O. J. o. t. C. S. Yahya, "An Enhanced Deep Neural Network Approach for WiFi Fingerprinting-Based Multi-Floor Indoor Localization," 2024.
B. Naresh, C. Ravikumar, P. L. Prasanna, B. S. Madhuri, S. Nimmala, and P. Srinivas, "Hybrid Deep Learning-Based Localization for Energy-Efficient Wireless Sensor Networks in Indoor IoT Environments," in 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2024: IEEE, pp. 407-412.
H. Rizk, A. Elmogy, and H. J. S. Yamaguchi, "A robust and accurate indoor localization using learning-based fusion of Wi-Fi RTT and RSSI," vol. 22, no. 7, p. 2700, 2022.
L. Bouse, S. A. King, and T. Chu, "Simplified Indoor Localization Using Bluetooth Beacons and Received Signal Strength Fingerprinting with Smartwatch," Sensors, vol. 24, no. 7, p. 2088, 2024.
Z. Zhang, Y. Yu, L. Chen, and R. Chen, "Hybrid Indoor Positioning System Based on Acoustic Ranging and Wi-Fi Fingerprinting under NLOS Environments," Remote Sensing, vol. 15, no. 14, p. 3520, 2023.
D. Gufran and S. Pasricha, "FedHIL: Heterogeneity resilient federated learning for robust indoor localization with mobile devices," ACM Transactions on Embedded Computing Systems, vol. 22, no. 5s, pp. 1-24, 2023.
R. Safwat, E. Shaaban, S. M. Al-Tabbakh, and K. Emara, "Fingerprint-based indoor positioning system using BLE: real deployment study," Bulletin of Electrical Engineering and Informatics, vol. 12, no. 1, pp. 240-249, 2023.
T. Perković, L. Dujić Rodić, J. Šabić, and P. Šolić, "Machine learning approach towards lorawan indoor localization," Electronics, vol. 12, no. 2, p. 457, 2023.
L. Li, X. Guo, and N. Ansari, "SmartLoc: Smart wireless indoor localization empowered by machine learning," IEEE Transactions on Industrial Electronics, vol. 67, no. 8, pp. 6883-6893, 2019.
M. A. Bhatti, R. Riaz, S. S. Rizvi, S. Shokat, F. Riaz, and S. J. Kwon, "Outlier detection in indoor localization and Internet of Things (IoT) using machine learning," Journal of Communications and Networks, vol. 22, no. 3, pp. 236-243, 2020.
S. Kurt and B. Tavli, "Path-Loss Modeling for Wireless Sensor Networks: A review of models and comparative evaluations," IEEE Antennas and Propagation Magazine, vol. 59, no. 1, pp. 18-37, 2017.
R. Gulia, Path loss model for 2.4 GHz indoor wireless networks with application to drones. Rochester Institute of Technology, 2020.
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 Rawaa Akram Mohammad Ali (Author); Negar Majma (Corresponding author); Aseel Hameed Al-Nakkash, Mohammadreza Soltanaghaei (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
